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Öğe Classification of vertebral column disorders and lumbar discs disease using attribute weighting algorithm with mean shift clustering(ELSEVIER SCI LTD, 2016) Unal, Yavuz; Polat, Kemal; Kocer, H. ErdincIn this article, a new data pre-processing method has been suggested to detect and classify vertebral column disorders and lumbar disc diseases with a high accuracy level. The suggested pre-processing method is called the Mean Shift Clustering-Based Attribute Weighting (MSCBAW) and is based primarily on mean shift clustering algorithm finding the number of the sets automatically. In this study, we have used two different datasets including lumbar disc diseases (with two classes-our database) and vertebral column disorders datasets (with two or three classes) taken from UCI (University of California at Irvine) machine learning database to test the proposed approach. The MSCBAW method is working as follows: first of all, the centres of the sets automatically for each characteristics in dataset by using the mean shift clustering algorithm are computed. And then, the mean values of each property in dataset are calculated. The weighted datasets by multiplying these mean values by each property value in the dataset that have been obtained by dividing the above mentioned mean values by the centres of the sets belonging to the relevant property are achieved. After the data weighting stage, three different classification algorithms that included the k-NN (k-Nearest Neighbour), RBF-NN (Radial Basis Function-Neural Network) and SVM (Support Vector Machine) classifying algorithms have been used to classify the datasets. In the classification of vertebral column disorders dataset with two classes (normal or abnormal), while the obtained classification accuracies and kappa values were 78.70% +/- 0.455 (the classification accuracy +/- standard deviation), 81.93% +/- 0.899, and 80.32% +/- 0.56 using SVM, k-NN (for k = 1), and RBF-NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF-NN classifiers were obtained 99.03% +/- 0.977, 99.67% +/- 0.992, and 99.35% +/- 0.9852, respectively. In the classification of second dataset named vertebral column disorders dataset with three classes (Normal, Disk Hernia, and Spondylolisthesis), while the obtained classification accuracies and kappa values were 74.51% +/- 0.581, 78.70% +/- 0.659, and 83.22% +/- 0.728 using SVM, k-NN (for k = 1), and RBF-NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF-NN classifiers were obtained 99.35% +/- 0.989, 96.77% +/- 0.948, and 99.67% +/- 0.994, respectively. As for the lumbar disc dataset, while the obtained classification accuracies and kappa values were 94.54% +/- 0.974, 94.54% +/- 0.877, and 93.45% +/- 0.856 using SVM, k-NN (for k = 1), and RBF-NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF-NN classifiers were obtained 100% +/- 1.00, 99.63% +/- 0.991, and 99.63% +/- 0.991, respectively. The best hybrid models in the classification of vertebral column disorders dataset with two classes, vertebral column disorders dataset with three classes, and lumbar disc dataset were the combination of MSCBAW and k-NN classifier, the combination of MSCBAW and RBF-NN classifier, and the combination of MSCBAW and SVM classifier, respectively. (C) 2015 Elsevier Ltd. All rights reserved.Öğe Detection of abnormalities in lumbar discs from clinical lumbar MRI with hybrid models(ELSEVIER, 2015) Unal, Yavuz; Polat, Kemal; Kocer, H. Erdinc; Hariharan, M.Disc abnormalities cause a great number of complaints including lower back pain. Lower back pain is one of the most common types of pain in the world. The computer-assisted detection of this ailment will be of great use to physicians and specialists. With this study, hybrid models have been developed which include feature extraction, selection and classification characteristics for the purpose of determining the disc abnormalities in the lumbar region. In determining the abnormalities, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people. In the feature extraction stage, 27 appearance characteristics and form characteristics were acquired from both sagittal and transverse images. In the feature selection stage, the F-Score-Based Feature Selection (FSFS) and the Correlation-Based Feature Selection (CBFS) methods were used to select the best discriminative features. The number of features was reduced to 5 from 27 by using the FSFS, and to 22 from 27 by using the CBFS. In the last stage, five different classification algorithms, i.e. the Multi-Layer Perceptron, the Support Vector Machine, the Decision Tree, the Naive Bayes, and the k Nearest Neighbor algorithms were applied. In addition, the combination of the classifier model (the combination of the bagging and the random forests) has been used to improve the classification performance in the detection of lumbar disc datasets. The results which were obtained suggest that the proposed hybrid models can be used safely in detecting the disc abnormalities. (C) 2015 Elsevier B.V. All rights reserved.Öğe Diagnosis of Pathology on the Vertebral Column with Backpropagation and Naive Bayes Classifier(IEEE, 2013) Unal, Yavuz; Kocer, H. ErdincMinimizing human errors in diagnosis is one of the most important aims of Computer-Aided Diagnosis (CAD) systems. CAD software is the software helping radiologists, specialist doctor for detect abnormality on medical views by using advanced pattern diagnosis and view processing methods. In this study, a CAD system that can help detection and diagnosis of Pathology on the Vertebral Column is introduced. The vertebral database used in the application is obtained from UCI (Machine Learning Repository). Various artificial intelligence methods were applied for the classification of this database obtained. The results obtained are comparatively explained in detail in the related part.Öğe Pairwise FCM based feature weighting for improved classification of vertebral column disorders(PERGAMON-ELSEVIER SCIENCE LTD, 2014) Unal, Yavuz; Polat, Kemal; Kocer, H. ErdincIn this paper, an innovative data pre-processing method to improve the classification performance and to determine automatically the vertebral column disorders including disk hernia (DH), spondylolisthesis (SL) and normal (NO) groups has been proposed. In the classification of vertebral column disorders' dataset with three classes, a pairwise fuzzy C-means (FCM) based feature weighting method has been proposed. In this method, first of all, the vertebral column dataset has been grouped as pairwise (DH-SL, DH-NO, and SL-NO) and then these pairwise groups have been weighted using a FCM based feature set. These weighted groups have been classified using classifier algorithms including multilayer perceptron (MLP), k-nearest neighbor (k-NN), Naive Bayes, and support vector machine (SVM). The general classification performance has been obtained by averaging of classification accuracies obtained from pairwise classifier algorithms. To evaluate the performance of the proposed method, the classification accuracy, sensitivity, specificity, ROC curves, and f-measure have been used. Without the proposed feature weighting, the obtained f-measure values were 0.7738 for MLP classifier, 0.7021 for k-NN, 0.7263 for Naive Bayes, and 0.7298 for SVM classifier algorithms in the classification of vertebral column disorders' dataset with three classes. With the pairwise fuzzy C-means based feature weighting method, the obtained f-measure values were 0.9509 for MLP, 0.9313 for k-NN, 0.9603 for Naive Bayes, and 0.9468 for SVM classifier algorithms. The experimental results demonstrated that the proposed pairwise fuzzy C-means based feature weighting method is robust and effective in the classification of vertebral column disorders' dataset. in the future, this method could be used confidently for medical datasets with more classes. (c) 2013 Elsevier Ltd. All rights reserved.
